Presented By O'Reilly and Cloudera
Make Data Work
5–7 May, 2015 • London, UK
 

Strata + Hadoop World in London 2015 Schedule

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Thursday, 7 May

King's Suite - Balmoral
10:55 A taste of random decision forests on Apache Spark Sean Owen (Cloudera)
11:45 Scalable machine learning Mikio Braun (Zalando SE)
13:45 Hunting criminals with hybrid analytics, semi-supervised learning, and agent feedback David Talby (Pacific AI), Claudiu Branzan (G2 Web Services)
14:05 Big data 2.0 democratizes machine learning technology for Wall Street Divanny Lamas (Context Relevant)
14:35 Poor man's parallel pipelines Jeroen Janssens (Data Science Workshops B.V.)
14:55 Users love Spark. Does Spark love (multiple) users? Richard Shaw (MapR)
16:15 Fast > Perfect: Practical approximation examples for mobile app analytics using Spark Streaming Kevin Schmidt (Mind Candy Ltd), Luis Angel Vicente Sanchez (Mind Candy Ltd.)
17:05 Untangling influence and desire: Visual analysis of massive graph data David Jonker (Uncharted Software Inc.), Scott Langevin (Uncharted Software)
King's Suite - Sandringham
10:55 How Goldman Sachs is using knowledge to create an information edge Joanne Hannaford (Goldman Sachs)
11:45 Information architecture for Apache Hadoop Mark Samson (Cloudera)
13:45 The Future of Apache Hadoop Security Joey Echeverria (Rocana)
14:35 Transparent encryption in HDFS Charles Lamb (Cloudera), Andrew Wang (Cloudera)
16:15 Adding insert, update, and delete to Hive Alan Gates (Hortonworks)
17:05 Big Data and IoT solutions in minutes Maarten Ectors (Canonical)
Buckingham Room - Palace Suite
10:55 Search evolved: Unraveling your data Costin Leau (Elastic)
11:45 Taming the firehose: Build analytics over 45 billion tweets using Elasticsearch and Spark Anirudh Koul (Microsoft), Shashank Singh (Microsoft)
13:45 SPARKTA: A real-time analytics platform based on Apache Spark Oscar Méndez (Stratio), David Morales (STRATIO)
14:35 Spark on Mesos Dean Wampler (Lightbend)
16:15 Say goodbye to batch Tyler Akidau (Google)
17:05 Introducing Apache Flink: Fast and reliable data analytics in clusters Stephan Ewen (data Artisans)
Blenheim Room - Palace Suite
10:55 The data strategy revolution: building an in-house data insights lab Nathan Shetterley (Accenture), Hallie Benjamin (Accenture), John Miller (Accenture)
11:45 The curiosity advantage: the most important skill for data science Oana Calugar (AliveShoes )
13:45 Data Strategy and the CDO Scott Kurth (Silicon Valley Data Science), Julie Steele (Silicon Valley Data Science)
14:35 Using data science to transform OpenTable into your local dining expert Sudeep Das (OpenTable)
16:15 Designing with data: A human-centered approach to data-driven design Matt Cooper-Wright (IDEO)
17:05 Sex, drugs and data: Using web data to add £3bn to the UK economy Andrew Fogg (import • io )
St. James / Regents
10:55 Algorithm ethics: The inevitable subjective judgments in analytics Majken Sander (Majken Sander), Joerg Blumtritt (Datarella)
11:45 Using data for EVIL Francine Bennett (Mastodon C), Duncan Ross (TES Global)
13:45 Sharing humanitarian data at the United Nations Francis Irving (ScraperWiki Ltd.)
14:35 Steady UX: Balancing personalisation and privacy to create understanding and trust Ann Wuyts (Sentiance)
16:15 Being a good data citizen Phil Harvey (Microsoft)
17:05 Visualizing the world's largest democratic exercise Anand  Subramanian  (Gramener)
Windsor Suite
10:55 The age of agile analytics has arrived! Frank Saeuberlich (Teradata)
11:45 Road to real-time digital business Rod Smith (IBM Emerging Internet Technologies )
13:45 Repeatedly Deliver Trusted and Timely Data for Big Data Analytics Scott Hedrick (Informatica), Mathieu Lagrange (Informatica)
14:35 A modern, flexible approach to Hadoop implementation, incorporating innovations from HP Vertica & IDOL Gilles Noisette (HP)
16:15 Apache Atlas: Data Governance for Hadoop Sean Roberts (Hortonworks)
Westminster Suite
14:35 Spark ask us anything Paco Nathan (O'Reilly Media), Patrick Wendell (Databricks)
Hilton Meeting Room 1-3
9:00 Apache Spark advanced training (Day 3) Olivier Girardot (Lateral Thoughts), Sameer Farooqui (Databricks)
Hilton Meeting Room 4-6
9:00 Practical Machine Learning (Day 2) Angie Ma (ASI), Marc Warner (ASI), Andrew Brookes (ASI Data Science), Anjali Samani (ASI), Alessandra Staglianò (The ASI), Ken Williams (Drayson Health ), Mahesan Niranjan (University of Southampton), Elena Chatzimichali (Wellcome Trust Sanger Institute, Cambridge)
8:00 Coffee break sponsored by SK Telecom
Room: King's Suite
9:00 Plenary
Room: King's Suite
Thursday Keynote Welcome Roger Magoulas (O'Reilly Media), Doug Cutting (Cloudera), Alistair Croll (Solve For Interesting)
9:05 Plenary
Room: King's Suite
British Telecom Featured Keynote Phillip Radley (BT)
9:15 Plenary
Room: King's Suite
Road to real-time digital business Rod Smith (IBM Emerging Internet Technologies )
9:25 Plenary
Room: King's Suite
Bringing life to design: Data science in 3D Mike Haley (Autodesk, Inc.)
9:35 Plenary
Room: King's Suite
Hadoop: It’s as easy as riding a bike Tamara Dull (SAS Institute Inc.)
9:40 Plenary
Room: King's Suite
Connected Car – World Record Race Gareth Martin (HP Enterprise Services)
9:45 Plenary
Room: King's Suite
Data Trends at Goldman Sachs Joanne Hannaford (Goldman Sachs)
9:55 Plenary
Room: King's Suite
Keynote with Christine Flounders Christine Flounders (Bloomberg LP)
10:05 Plenary
Room: King's Suite
Is Privacy Becoming a Luxury Good? Julia Angwin (ProPublica)
10:25 Morning break sponsored by SAS
Room: Monarch Suite
15:15 Afternoon sponsored by HP
Room: Monarch Suite
12:25 Lunch sponsored by IBM
Room: Sponsor Pavilion / Westminster Suite / Fiamma Restaurant
Thursday Lunchtime BoF Tables (located in the Monarch Suite)
10:55-11:35 (40m) Data Science
A taste of random decision forests on Apache Spark
Sean Owen (Cloudera)
Apache Spark has a lot to like for the data scientist: natively distributed, REPL, Scala and Python APIs, and a machine learning library, MLlib. Spark 1.2 includes an implementation of random decision forests, an important classifier/regressor algorithm. This talk will introduce Spark, Scala, and random decision forests, and demonstrate the process of analyzing a real-world data set with them.
11:45-12:25 (40m) Data Science
Scalable machine learning
Mikio Braun (Zalando SE)
While the data management side of Big Data has seen tremendous progress in the past few years, bringing technologies like Hadoop or Spark together with advanced machine learning and data analysis methods is still a major challenge. In this talk, I will discuss recent advances, approaches, and patterns which are used to build truly scalable machine learning solutions.
13:45-14:05 (20m) Data Science
Hunting criminals with hybrid analytics, semi-supervised learning, and agent feedback
David Talby (Pacific AI), Claudiu Branzan (G2 Web Services)
Live demo using Python open-source libraries to build a hybrid machine-learning model for fraud detection, combining features from natural language processing, topic modeling, time series analysis, link analysis, heuristic rules, and anomaly detection. We’ll then show how we scaled to billions of events using Spark, and what it took to make the system perform and ready for production.
14:05-14:25 (20m) Data Science
Big data 2.0 democratizes machine learning technology for Wall Street
Divanny Lamas (Context Relevant)
Context relevant has defined the next-generation of financial information capabilities by applying rapid automated predictive analytics software to solve Wall Street’s toughest problems. The big data 2.0 era of automated, intelligent, and scalable systems allows Wall Street banks to finally take advantage of the massive value of the data they hold and better serve and protect their customers.
14:35-14:55 (20m) Data Science
Poor man's parallel pipelines
Jeroen Janssens (Data Science Workshops B.V.)
Hadoop, Storm, and Spark are fantastic frameworks for processing massive amounts of data in parallel. Every now and then, there is a one-off data science task that could really use some speeding up. For those kinds of tasks, it's probably not worthwhile to set up large frameworks. This presentation demonstrates GNU Parallel, which allows you to easily parallelize and distribute such tasks.
14:55-15:15 (20m) Data Science
Users love Spark. Does Spark love (multiple) users?
Richard Shaw (MapR)
Apache Spark is a powerful, unified data processing engine offering a number of APIs, from batch/SQL over streaming to manipulations over graphs. The core architecture of Spark has not necessarily been designed with a multi-user environment in mind. We will review existing and emerging approaches how to use Spark in multi-user environments, such as the Tachyon project.
16:15-16:55 (40m) Data Science
Fast > Perfect: Practical approximation examples for mobile app analytics using Spark Streaming
Kevin Schmidt (Mind Candy Ltd), Luis Angel Vicente Sanchez (Mind Candy Ltd.)
Mobile gaming is a fast-moving field and needs metrics like daily active users or revenue in real-time to be able to fine-tune quickly. Approximation is needed to count those metrics, as the data volume would be too large to process exactly in real-time. We will demonstrate how to use Spark Streaming and probabilistic data structures to achieve a low error rate, even for many millions of users.
17:05-17:45 (40m) Data Science
Untangling influence and desire: Visual analysis of massive graph data
David Jonker (Uncharted Software Inc.), Scott Langevin (Uncharted Software)
This session demonstrates using open source tools and techniques for visually exploring massive node-link graphs in a web browser by visualizing all the data. Seeing all the data reveals informative patterns and provides important context to understanding insights. Examples will highlight large scale graph analysis of social networks, customer purchase history, and health care industry data.
10:55-11:35 (40m) Hadoop Platform
How Goldman Sachs is using knowledge to create an information edge
Joanne Hannaford (Goldman Sachs)
Goldman Sachs is a leading global investment banking, securities, and investment management firm that provides a wide range of financial services. Goldman executes hundreds of millions of financial transactions per day across nearly every market in the world. Learn how Goldman is harnessing knowledge, data, and compute power to maintain and increase its competitive edge.
11:45-12:25 (40m) Hadoop Platform
Information architecture for Apache Hadoop
Mark Samson (Cloudera)
The Hadoop ecosystem makes it possible to build an enterprise data hub capable of storing and analysing a wide variety of data. However, a platform with such broad capability triggers a question: how to organise the myriad data sets in a way that allows users to explore and access the data they need? This session will propose an information architecture for Hadoop that enables this.
13:45-14:25 (40m) Hadoop Platform
The Future of Apache Hadoop Security
Joey Echeverria (Rocana)
As the volume of data and number of applications moving to Apache Hadoop has increased, so has the need to secure that data and those applications. In this presentation, we'll take a brief look at where Hadoop security is today and then peer into the future.
14:35-15:15 (40m) Hadoop Platform
Transparent encryption in HDFS
Charles Lamb (Cloudera), Andrew Wang (Cloudera)
Encryption is a requirement for many business sectors dealing with confidential information. To meet these requirements, transparent, end-to-end encryption was added to HDFS. This protects data while it is in-flight and at-rest, and can be used compatibly with existing Hadoop apps. We will cover the design and implementation of transparent encryption in HDFS, as well as performance results.
16:15-16:55 (40m) Hadoop Platform
Adding insert, update, and delete to Hive
Alan Gates (Hortonworks)
Starting in Hive 0.14, insert values, update, and delete have been added to Hive SQL. In addition, ACID compliant transactions have been added so users get a consistent view of data while reading and writing. This talk will cover the intended use cases, architecture, and performance of insert, update, and delete in Hive.
17:05-17:45 (40m) Tools & Technology
Big Data and IoT solutions in minutes
Maarten Ectors (Canonical)
What if Big Data technologies would be like Lego blocks that can be clicked together to create complete solutions. You could add continuous deployment, e.g. Pig or Storm topologies. Integrate SSO. You can add sentiment analysis or real-time dashboards. You can integrate any data source. All Open Source. We are ready to demo this so Big Data solutions in minutes is reality not marketing.
10:55-11:35 (40m) Hadoop & Beyond
Search evolved: Unraveling your data
Costin Leau (Elastic)
Search is more than typing words into a box. It's evolved into the backbone for today’s analytics demands​ and is an asset for businesses ​to ​ask the right questions ​in order to make sense of their data. Versatile, agile search and analytics can uncover the “uncommonly common” trends within, giving businesses real-time insights and setting them up to make the right data-driven decisions.
11:45-12:25 (40m) Hadoop & Beyond
Taming the firehose: Build analytics over 45 billion tweets using Elasticsearch and Spark
Anirudh Koul (Microsoft), Shashank Singh (Microsoft)
We share lessons learned the hard way while building a real-time search, analytics, and trends pipeline over social media posts, using Elasticsearch, Azure, and Spark Streaming. Topics cover building an end-to-end pipeline including stream processing, applying natural language processing tools, scaling and performance tuning, search relevance, and applications like TV trends.
13:45-14:25 (40m) Hadoop & Beyond
SPARKTA: A real-time analytics platform based on Apache Spark
Oscar Méndez (Stratio), David Morales (STRATIO)
Nowadays, all kinds of businesses need to deal with real-time information in order to successfully deliver their core services. SPARKTA was born to meet this demand. Thanks to this technology, real-time analysis is readily available for every use case with absolutely no coding. SPARKTA is easy to deploy, and also open source, fast, scalable, and fault-tolerant.
14:35-15:15 (40m) Hadoop & Beyond
Spark on Mesos
Dean Wampler (Lightbend)
Spark is often seen as a replacement for MapReduce in Hadoop systems, but Spark clusters can also be deployed and managed by Mesos. This talk explains how to use Mesos for Spark applications. Using example applications, we'll examine the pros and cons of using Mesos vs. Hadoop YARN as a data platform and discuss practical issues when running Spark on Mesos.
16:15-16:55 (40m) Hadoop & Beyond
Say goodbye to batch
Tyler Akidau (Google)
Learn what it takes to ditch your Big Data batch pipelines and go all-streaming-all-the-time, without compromising latency, correctness, or the flexibility to deal with changes in upstream data.
17:05-17:45 (40m) Hadoop & Beyond
Introducing Apache Flink: Fast and reliable data analytics in clusters
Stephan Ewen (data Artisans)
Apache Flink is a data analysis engine designed to match Hadoop in reliability and Spark in performance. Flink introduces novel features such as cost-based optimization for Java and Scala programs, native iterative processing, unification of streaming and batch processing, and efficient hybrid in-memory/on-disk processing. Flink has more than 70 contributors from industry and academia.
10:55-11:35 (40m) Business & Industry
The data strategy revolution: building an in-house data insights lab
Nathan Shetterley (Accenture), Hallie Benjamin (Accenture), John Miller (Accenture)
In this talk, John, Nate, and Hallie from Accenture's Technology Labs will explain their perspective on existing approaches to data strategy, and how a devoted data innovation lab can pull together open source technology and open data, create a visualization, and mock up a prototype, to help organizations pave the way for exploration of new data frontiers.
11:45-12:25 (40m) Business & Industry
The curiosity advantage: the most important skill for data science
Oana Calugar (AliveShoes )
Curiosity is one of the most valued skills for people working in Data Science. But how can we train it? Einstein said that "Curiosity is an important trait of a genius". Let’s explore how we can develop our curiosity with three exercises in the session: how to find pleasure in uncertainty; question the question we’re asking; and find a beginner's mind. With direct application to data science.
13:45-14:25 (40m) Business & Industry
Data Strategy and the CDO
Scott Kurth (Silicon Valley Data Science), Julie Steele (Silicon Valley Data Science)
As the necessity of having a data strategy is sinking in, the chief data officer (CDO) has emerged as a new member of the executive team focused on creating and implementing that strategy. This talk describes what that looks like across a variety of industries and organizations, and shares some best practices for getting the most out of your business data.
14:35-15:15 (40m) Business & Industry
Using data science to transform OpenTable into your local dining expert
Sudeep Das (OpenTable)
I will talk about how we are using data science to help transform OpenTable into a local dining expert who knows you very well, and can help you and others find the best dining experience wherever you travel. This entails a whole slew of tools from natural language processing, recommendation system engineering, sentiment analysis to predictions based on internal and external signals!
16:15-16:55 (40m) Business & Industry
Designing with data: A human-centered approach to data-driven design
Matt Cooper-Wright (IDEO)
IDEO's Hybrid team brings all the design tools from IDEO's product design process to work with clients on data oriented projects. The team will share elements of their process and case studies to show how incorporating human-centered techniques from design can improve data as an input to decision making.
17:05-17:45 (40m) Business & Industry
Sex, drugs and data: Using web data to add £3bn to the UK economy
Andrew Fogg (import • io )
How much does prostitution contribute to the UK economy? According to the UK’s Office of National Statistics the answer is £5bn, or 0.4% of GDP. But how did they calculate that number? With 10-year-old survey data and lots of assumptions, that's how. In this talk Andrew Fogg shows how he was able to add £3bn to the UK economy using some statistical sleuthing and modern web data techniques.
10:55-11:35 (40m) Privacy, Law, & Ethics
Algorithm ethics: The inevitable subjective judgments in analytics
Majken Sander (Majken Sander), Joerg Blumtritt (Datarella)
Algorithms define the meaning we get from data. Arbitrary decisions are regularly built into our analytics by chosen method, setting parameters, or dealing with missing values. These value judgments are not present in the privacy discussion or business point of view. However, they may be much more important than the more obvious data collection or secure storage.
11:45-12:25 (40m) Privacy, Law, & Ethics
Using data for EVIL
Francine Bennett (Mastodon C), Duncan Ross (TES Global)
In Barcelona we saw that being good is hard. Being evil is fun and gets you paid more. Over the last 18 months examples have been embarrassingly easy to find. We review the field of doing high-impact evil with data and analysis. Make the maximum (negative) impact on your friends, your business, and the world in this updated version of the best talk from 2013.
13:45-14:25 (40m) Privacy, Law, & Ethics
Sharing humanitarian data at the United Nations
Francis Irving (ScraperWiki Ltd.)
Better data collaboration is vital for every organization. For the UN's Humanitarian division it is particularly hard--they work in hundreds of countries, in emergencies and natural disasters. This talk describes the Humanitarian Data Exchange, answering such questions as: what motivates busy, front-line staff to share data? How do you measure the success of a data collaboration platform?
14:35-15:15 (40m) Privacy, Law, & Ethics
Steady UX: Balancing personalisation and privacy to create understanding and trust
Ann Wuyts (Sentiance)
'Connected' refers more and more to a human-machine relationship that requires understanding and trust; personalisation and respect for what is personal. More about this changing relationship, six basic concepts that apply to user experience design as well as privacy, and tips for delivering both understanding and trust.
16:15-16:55 (40m) Privacy, Law, & Ethics
Being a good data citizen
Phil Harvey (Microsoft)
Data is hard. Old thinking and old tools play their part, but the worst offenders are bad data citizens. This talk calls out the bad behavior and old thinking. Then it covers new thinking, a better way of approaching tools, and, most importantly, how to make data easy by being a good data citizen.
17:05-17:45 (40m) Privacy, Law, & Ethics
Visualizing the world's largest democratic exercise
Anand  Subramanian  (Gramener)
The election results page for the 2014 Indian general elections was hosted on CNN-IBN and bing.com. The focus was on real-time analysis of results for users and TV anchors. With over 540 million voters and 100 million viewers, the volume and complexity of data both provide a design challenge. This talk focuses on the techniques behind this design. http://blog.gramener.com/1755
10:55-11:35 (40m) Sponsored
The age of agile analytics has arrived!
Frank Saeuberlich (Teradata)
Most organizations nowadays see the massive value potential in (big) data analytics. What most of them still fear is that starting an analytics initiative will result in a massive IT project that will take 12-18 months before first analytical results are achieved – and deploying the results to generate business value will take another 12-18 months. . .
11:45-12:25 (40m) Sponsored
Road to real-time digital business
Rod Smith (IBM Emerging Internet Technologies )
Big data and analytics continue to be a disruptive business force. Are we entering another phase – real-time digital business transformation, where businesses are realizing that the time to adjust to market and customer opportunities and threats is shrinking quickly?
13:45-14:25 (40m) Sponsored
Repeatedly Deliver Trusted and Timely Data for Big Data Analytics
Scott Hedrick (Informatica), Mathieu Lagrange (Informatica)
Presentation focusing on the opportunities and challenges presented by big data, featuring a technical overview of how Informatica can help deliver trusted and timely data and including examples of customer best practice.
14:35-15:15 (40m) Sponsored
A modern, flexible approach to Hadoop implementation, incorporating innovations from HP Vertica & IDOL
Gilles Noisette (HP)
HP has integrated Hadoop into the core of our Big Data platform, solutions and services. We will introduce the HP Big Data Reference Architecture and a series of services that will accelerate your adoption of Hadoop. HP's new BDRA for Hadoop offers an extremely flexible and powerful platform, when HP Haven Big Data Software solutions can be used to augment Hadoop and build a smarter Data Lake.
16:15-16:55 (40m) Sponsored
Apache Atlas: Data Governance for Hadoop
Sean Roberts (Hortonworks)
Apache Atlas proposes to provide governance capabilities in Hadoop that use both a prescriptive and forensic models enriched by business taxonomical metadata.
14:35-15:15 (40m) Ask Us Anything
Spark ask us anything
Paco Nathan (O'Reilly Media), Patrick Wendell (Databricks)
Join the Spark team for an informal question and answer session. Spark committers from Databricks will be on hand to field a wide range of detailed questions. Even if you don't have a specific question, join in to hear what others are asking.
9:00-17:00 (8h) Training
Apache Spark advanced training (Day 3)
Olivier Girardot (Lateral Thoughts), Sameer Farooqui (Databricks)
This three-day curriculum features advanced lectures and hands-on technical exercises for advanced Spark usage in data exploration, analysis, and building big data applications. Course materials emphasize architectural design patterns and best practices for leveraging Spark in the context of other popular, complementary frameworks for building and managing enterprise data workflows.
9:00-17:00 (8h) Training
Practical Machine Learning (Day 2)
Angie Ma (ASI), Marc Warner (ASI), Andrew Brookes (ASI Data Science), Anjali Samani (ASI), Alessandra Staglianò (The ASI), Ken Williams (Drayson Health ), Mahesan Niranjan (University of Southampton), Elena Chatzimichali (Wellcome Trust Sanger Institute, Cambridge)
This intensive two-day course will provide you with a condensed introduction to the key concepts and techniques of machine learning. It will allow you to know what is and is not possible with these exciting new tools, and understand how they can benefit your organization. It will give you the language and framework to talk to both experts and executives.
8:00-9:00 (1h)
Break: Coffee break sponsored by SK Telecom
9:00-9:05 (5m)
Thursday Keynote Welcome
Roger Magoulas (O'Reilly Media), Doug Cutting (Cloudera), Alistair Croll (Solve For Interesting)
Program Chairs Roger Magoulas, Doug Cutting, and Alistair Croll welcome you to the second day of keynotes.
9:05-9:15 (10m)
British Telecom Featured Keynote
Phillip Radley (BT)
In this session, Phill Radley (Chief Data Architect at British Telecom) gives an overview of BT's internal multi-tenant hadoop platform. He explains their first production use case (master data management of BT UK Business Customer data) and gives a flavour of their use case pipeline.
9:15-9:25 (10m) Sponsored
Road to real-time digital business
Rod Smith (IBM Emerging Internet Technologies )
Big data and analytics continue to be a disruptive business force. Are we entering another phase – real-time digital business transformation, where businesses are realizing that the time to adjust to market and customer opportunities and threats is shrinking quickly?
9:25-9:35 (10m)
Bringing life to design: Data science in 3D
Mike Haley (Autodesk, Inc.)
Jet engines, lifelike movie monsters, cancer-fighting nanorobots, and bespoke products. We live in a world where everything around us is designed by someone. The pace of innovation is escalating and with new methods of manufacturing, such as 3D printing, the demands placed on designers and design technology are increasing.
9:35-9:40 (5m) Sponsored
Hadoop: It’s as easy as riding a bike
Tamara Dull (SAS Institute Inc.)
Join SAS’s Tamara Dull as she compares bike riding to current trends in big data adoption and explains why newer technologies like Hadoop aren’t always to blame.
9:40-9:45 (5m) Sponsored
Connected Car – World Record Race
Gareth Martin (HP Enterprise Services)
As the internet of things and connected car programs across the globe gain momentum and broaden in scope, check out this world record attempt; racing from North Cape, Norway to Cape Agulhas, South Africa. . .
9:45-9:55 (10m)
Data Trends at Goldman Sachs
Joanne Hannaford (Goldman Sachs)
Joanne Hannaford, Partner, Goldman Sachs.
9:55-10:05 (10m)
Keynote with Christine Flounders
Christine Flounders (Bloomberg LP)
Christine Flounders, Bloomberg
10:05-10:20 (15m)
Is Privacy Becoming a Luxury Good?
Julia Angwin (ProPublica)
We are being watched – by companies, by the government, by our neighbors. Technology has made powerful surveillance tools available to everyone. And now some of us are investing in counter-surveillance techniques and tactics.
10:25-10:55 (30m)
Break: Morning break sponsored by SAS
15:15-16:15 (1h)
Break: Afternoon sponsored by HP
12:25-13:45 (1h 20m) Events
Thursday Lunchtime BoF Tables (located in the Monarch Suite)
Birds of a Feather (BoF) discussions are a great way to informally network with people in similar industries or interested in the same topics. (Located in the Monarch Suite)